In response to the current environmental challenges, city governments worldwide are developing action plans to both reduce GHG emissions and increase the resilience of their built environment. Given the relevance of energy use in buildings, such plans introduce a variety of efficiency and supply planning strategies ranging from the scale of buildings, to full districts. Their implementation requires information about current building energy demands, and how these demands, and the city's energy ecosystem at large, may change as a result of a specific urban intervention. Unfortunately, metered data is not available at a sufficient level of detail, and cities face an "information gap" between the aggregate scale of their emission targets, and the scale of implementation of energy strategies. To close it, municipalities and other interested stakeholders require modeling tools that provide accurate spatially and temporally defined energy demands by building. Urban Building Energy Models (UBEMs) have been proposed in research as a bottom-up, physics based, urban modeling technique, to estimate energy demands by building for current conditions and future scenarios. Given the large number of data inputs required in their generation, UBEMs have relied on their characterization through "building archetypes". Yet, in the absence of detailed building and energy data, this process has remained somewhat arbitrary, relying on deterministic assumptions and the subjective judgement of the modeler. The resulting simplification can potentially lead to predictions that misrepresent urban demands and misinform decision makers. In order to address these limitations and enable the large scale application of UBEMs, this dissertation introduces a set of modeling and calibration techniques. First, in order to demonstrate the feasibility of citywide municipal UBEMs, an 80,000 buildings model is generated and simulated for the city of Boston, based exclusively on currently available and maintained municipal datasets. An automated modeling workflow and a new library file format for archetypes are developed for that purpose, and current limitations of municipal datasets and practices are identified. To improve the reliability of UBEMs in reproducing metered demands, a new calibration approach is proposed, which applies principles of Bayesian statistics to reduce the uncertainty in archetype parameters defined stochastically based on a sample of metered buildings. The method is demonstrated and validated in the model of a residential district in Kuwait with 323 annually metered buildings, showing errors below 5% in the mean and 15% in the variance when compared with the measured EUI distribution. The accuracy of the resulting UBEM when reproducing EUI distributions is also compared with typical deterministic approaches, resulting in an error improvement of 30-40%. The method is expanded for its application when monthly energy data is available, and applied for the calibration of a sample including 2,662 residential buildings in Cambridge, MA. Finally, the relevance of calibrated archetype-based UBEMs in urban decisions is discussed from the perspectives of policy makers, energy providers, urban designers and real estate owners in two application cases in neighborhoods of Kuwait City and Boston.